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Found 1,154 Skills
Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Use when asked to "create a skill", "write a skill", "synthesize sources into a skill", "improve a skill from positive/negative examples", "update a skill", or "maintain skill docs and registration". Handles source capture, depth gates, authoring, registration, and validation.
Access 1200+ AI Agent tools via Model Context Protocol (MCP)
Run the sefirot loop and confirm with the user if there are any questions
Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.
Interact with the Paperclip control plane API to manage tasks, coordinate with other agents, and follow company governance. Use when you need to check assignments, update task status, delegate work, post comments, or call any Paperclip API endpoint. Do NOT use for the actual domain work itself (writing code, research, etc.) — only for Paperclip coordination.
OpenClaw learning expert that retrieves and synthesizes information from official documentation (https://docs.openclaw.ai) and GitHub repository (https://github.com/openclaw/openclaw). Use this skill whenever the user asks questions about OpenClaw, including installation, configuration, API usage, concepts, troubleshooting, best practices, or any OpenClaw-related inquiries. Triggers include OpenClaw questions about features, implementation, usage, setup, or any openclaw-related topics.
Apply DriveMind, the calm reliability layer for AI agents. Use when a task needs steady follow-through, clearer progress, stronger persistence without recklessness, explicit safety boundaries, human-in-the-loop collaboration, post-task review, reusable memory, or when the user says things like 'keep pushing', 'don’t stop too early', 'be steady', 'if risk is unclear ask me', 'review this after', or 'write down the lesson'.
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
Build and deploy agentic finance applications on the Alva platform. Access 250+ financial data sources (crypto, equities, macro, on-chain, social), run cloud-side analytics, backtest trading strategies, and release interactive playbooks -- all from your AI agents.
A guided, zero-friction installer and maintenance assistant for OpenClaw. Use this skill when the user wants to install OpenClaw, set up OpenClaw on a local machine or remote server, connect OpenClaw to DingTalk, get OpenClaw skill recommendations for their use case, or perform post-installation maintenance (health checks, troubleshooting, installing new skills, changing AI models, adding chat channels, updating OpenClaw). Handles full environment detection, installation, optional DingTalk integration, scene-based skill recommendations, and daily maintenance — all interactively, with no wasted steps.
Uses persistent markdown files for general planning, progress tracking, and knowledge storage (Manus-style workflow). Use for multi-step tasks, research projects, or general organization WITHOUT mentioning PRD. For PRD-specific work, use prd-planner skill instead.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.